Recent advances in deep learning technology are progressing in MAR. [1]} used deep learning to generate an artifact-reduced prior image, then used the projection of the prior image to replace the metal-affected projection, and then performs the final MAR CT reconstruction. [2]} performed a residual learning to correct metal streaking artifacts after the first pass by normalized metal artifact reduction (NMAR). Metal artifacts are non-local and highly associated with various factors, including the geometry of metallic inserts and the energy spectrum of the incident X-ray beam, making it difficult to learn their complicated structures in the image domain.
